The World Wide Web became broadly available in August 1991, ushering in the Information Age and catalyzing profound changes in almost every aspect of human endeavor. This new age is characterized by online connectivity, real‐time interactions, and the convergence of information and communication technology, institutions, people, and processes (Castells 2003). Statistics from 2020 show that approximately 58.8% of the global population (a little over 4.5 billion people) use the internet (https://internetworldstats.com/stats.htm). Most of these people leave digital traces of their interests and attitudes in the form of, for example, the websites they visit, the photos and comments they post on social media platforms, and the videos of their activities they record and share online. Much of these data are publicly available and, with application of some technical expertise, can yield fascinating insights into human culture, including interactions and attitudes toward nature and conservation.

The quantification of human culture through the analysis of vast digital corpora has been termed “culturomics” (Michel et al. 2011) and “conservation culturomics” is the application of these methods to the study of human–nature interactions (Ladle et al. 2016). As the frequency and quality of encounters with wild nature decrease (Soga & Gaston 2016), individual knowledge, attitudes, beliefs, and behaviors toward nature and nature conservation are increasingly shaped by experiences in the virtual world. Conservation culturomics is uniquely placed to support monitoring and reactions to changes in the environment and human–nature interactions. Such new capabilities allow conservation organizations to proactively enact policy and management to tackle emerging threats and harness new opportunities. Moreover, the power of culturomic analysis to detect these changes will improve as internet access increases globally, new tools become available, and digital corpora expand in number, coverage, and volume.

A review of conservation culturomics research (Appendix S1) shows that this area of study is still in its early stages, but publications have increased rapidly (Fig. 1a) and already cover a remarkable range of subjects (Fig. 1b). These include assessing public interest in species and support for their conservation, understanding human uses of natural areas and the values derived from such interactions, and evaluating the prominence and dynamics of conservation‐related topics in political discourse (see Appendix S2 for a list of conservation culturomics publications and their attributes). Conservation culturomics research has been carried out from site‐based to global spatial extents, taking advantage of the increasingly global reach of the internet (Appendix S3), and has examined plants, fungi, invertebrates, and aquatic and terrestrial vertebrates (Fig. 1c). For this, researchers have used data from diverse sources, such as social media platforms, digital encyclopedias, internet web pages, and search engines, and even sports‐tracking applications (Fig. 1d & Appendix S4). Nevertheless, there are numerous ways conservation culturomics research can be expanded in reach and scope.

Articles in the special section on conservation culturomics advance this emerging field through at least 2 key aspects. The first is methodological, by outlining and expanding the conservation culturomics toolbox. Correia et al. (2021) provide an overview of the data sources and methods available for data collection and analysis and the challenges associated with this type of research. They also outline some of the upcoming challenges for this emerging research area. Mittermeier et al. (2021) focus specifically on the Wikipedia platform, exploring its utility as a source of data for culturomics. Wikipedia allows open access to its raw data, an advantage over many other sources of culturomic data, but has particular characteristics and limitations. The authors suggest several methodological approaches for analysis of Wikipedia data and exemplify them by exploring trends in Wikipedia page views of birds globally. Väisänen et al. (2021) highlight the potential of automated image identification for culturomics. Images are increasingly a relevant source of data, and they clearly illustrate this potential through an analysis of thousands of photos taken by local and foreign visitors to Finnish national parks. In doing so, they disentangle how different groups of people interact with and obtain value from these diverse protected areas. Di Minin et al. (2021) address the privacy concerns arising from use of personal digital data and provide clear guidelines on how to address these concerns. Taking social media data as an example, they provide specific recommendations on how to handle personal data that are in line with the European Union's General Data Protection Regulation.

The second key aspect of the special section is its expansion and enhancement of the scope of culturomics applications. Specifically, Jarić et al. (2021) show how culturomics tools, approaches, and data can be applied to the challenges posed by invasive non‐native species. They highlight how culturomics data can be used to find and identify such species and to gauge people's attitudes and interactions with them, both of which have potentially important consequences for early assessment, control, and eradication of invasive species. Li and Hu (2021) use culturomics to improve understanding of the human dimensions associated with the consumption of illegal wildlife products. Using social media data and focusing on tiger bone wine, they explore the profile of the people purchasing these products and the factors motivating their consumption. Vardi et al. (2021) provide a unique and detailed assessment of public interest in Israeli wildflowers in a comparison of metrics derived from Wikipedia and Google. They found clear seasonal trends associated with interest in wildflowers that are associated with their natural phenology and that these trends differ between platforms based on the types of digital engagement people have with nature. Finally, Millard et al. (2021) propose a new indicator of public awareness of biodiversity derived from Wikipedia data. They developed this indicator to help monitor progress toward Aichi target 1, which pertains to public awareness of biodiversity and adopts a similar approach to the Living Plant Index's assessments of natural populations over time. Significantly, Millard et al. (2021) also propose how this indicator can be integrated with other existing monitoring tools based on data derived from other digital platforms.

Conservation of the natural world is faced with vast, complex, and expanding problems. These problems demand new and creative solutions, and culturomics approaches to the study of human–nature relationships provide one way forward. The diverse and novel contributions in this special section represent an important milestone in the development of conservation culturomics. They introduce this field of research to a broader readership and raise awareness about its tools and applications. We hope the articles will stimulate debate and discussion and encourage more researchers to engage with this rapidly expanding conservation research area.

1991年8月，万维网开始广泛使用，这标志着信息时代的来临，并在人类活动的几乎每个方面都催生了深刻的变化。这个新时代的特点是在线连接，实时交互以及信息和通信技术，机构，人员和流程的融合（Castells 2003）。2020年的统计数据显示，全球约有58.8％（略超过45亿的人口）使用互联网（https://internetworldstats.com/stats.htm）。其中大多数人以其访问的网站，在社交媒体平台上发布的照片​​和评论以及在线记录和共享的活动视频的形式留下他们的兴趣和态度的数字化痕迹。这些数据大部分是公开可用的，并且通过应用某些技术专长，可以对人类文化产生令人着迷的见解，包括对自然和保护的相互作用和态度。

通过分析庞大的数字语料库对人类文化的量化被称为“文化学”（Michel等人，2011年），“保护文化学”是将这些方法应用于人与自然相互作用的研究（Ladle等人，2016年）。）。随着与野生大自然相遇的频率和质量下降（Soga＆Gaston 2016），个人对自然和自然保护的知识，态度，信念和行为越来越多地受到虚拟世界中经验的影响。保护文化的独特之处在于支持监测和对环境变化以及人与自然相互作用的反应。这些新功能使保护组织能够主动制定政策和管理措施，以应对新出现的威胁并把握新机遇。此外，随着全球互联网访问的增加，新工具的出现以及数字语料库的数量，覆盖范围和数量的增加，文化分析法检测这些变化的能力将会提高。

对保护性文化研究的回顾（附录S1）表明，该研究领域仍处于早期阶段，但是出版物迅速增加（图1a），并且已经涵盖了相当多的主题（图1b）。这些措施包括评估公众对物种的兴趣和对物种保护的支持，了解人类对自然区域的利用以及从这些相互作用中获得的价值，以及评估政治话题中与保护相关的主题的重要性和动态（有关保护的清单，请参阅附录S2）文化学出版物及其属性）。利用互联网日益广泛的影响力（附录S3），已经从站点到全球空间范围进行了保护文化研究，并检查了植物，真菌，无脊椎动物，以及水生和陆生脊椎动物（图1c）。为此，研究人员使用了来自各种来源的数据，例如社交媒体平台，数字百科全书，互联网网页和搜索引擎，甚至是体育跟踪应用程序（图1d和附录S4）。然而，有多种方法可以扩大保护文化研究的范围和范围。

关于保护文化的特别章节中的文章至少通过两个关键方面推动了这一新兴领域的发展。首先是方法论，通过概述和扩展保护文化学工具箱。Correia等。（2021年）概述了可用于数据收集和分析的数据源和方法，以及与此类研究相关的挑战。他们还概述了该新兴研究领域即将面临的挑战。Mittermeier等。（2021年）特别关注Wikipedia平台，探索其作为文化数据来源的实用性。维基百科允许开放访问其原始数据，这比许多其他文化数据来源具有优势，但具有特定的特征和局限性。作者提出了几种分析维基百科数据的方法论方法，并通过探索全球鸟类的Wikipedia页面浏览趋势来举例说明。Väisänen等。（2021年）强调了自动图像识别技术对培养物的潜力。图片越来越成为相关的数据来源，并且通过对芬兰国家公园的本地和外国游客拍摄的数千张照片进行分析，清楚地说明了这种潜力。这样一来，他们就可以弄清楚不同人群如何与这些不同的保护区互动并从中获得价值。Di Minin等。（2021）解决了因使用个人数字数据而引起的隐私问题，并提供了有关如何解决这些问题的明确指南。以社交媒体数据为例，它们提供了有关如何处理符合欧盟《通用数据保护条例》的个人数据的具体建议。

特别部分的第二个关键方面是它扩大和扩大了文化学应用的范围。具体来说，Jarić等人。（2021）展示了文化学的工具，方法和数据如何被用于应对非本地入侵物种带来的挑战。它们强调了如何使用文化数据来发现和识别此类物种以及评估人们的态度以及与之的互动，这两者对于早期评估，控制和根除入侵物种具有潜在的重要影响。栗胡（2021）使用文化疗法来增进人们对与非法野生生物产品消费相关的人的维度的了解。他们使用社交媒体数据并专注于虎骨酒，探索了购买这些产品的人们的概况以及刺激其消费的因素。Vardi等。（2021）通过比较从Wikipedia和Google得出的指标，对以色列野花的公共利益进行了独特而详尽的评估。他们发现与自然物候相关的野花趣味相关的明显季节性趋势，并且这些趋势在平台之间会有所不同，具体取决于人们对自然的参与程度。最后，米拉德等。（2021年）提出一项新的指标，以提高公众对维基百科数据中生物多样性的认识。他们开发了该指标，以帮助监测实现爱知目标1的进展，该目标1与公众对生物多样性的认识有关，并且随着时间的流逝，对活动植物指数对自然种群的评估也采用了类似的方法。值得注意的是，米拉德等。（2021）还提出了如何基于从其他数字平台获得的数据将该指标与其他现有监视工具集成。

保护自然世界面临着巨大，复杂和不断扩大的问题。这些问题需要创新的解决方案，而人文关系研究的文化学方法提供了一种前进的途径。在这一特殊部分中，各种新颖的贡献代表了保护文化的发展的重要里程碑。他们向更广泛的读者介绍了这一研究领域，并提高了对其工具和应用程序的认识。我们希望这些文章能引起辩论和讨论，并鼓励更多的研究人员参与这一迅速扩展的保护研究领域。